{
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"
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# 主要工作\n",
    "\n",
    "### 一、数据预处理：\n",
    "\n",
    "#### 时间差分划分数据集\n",
    "    1. 测试集是2016年4月14日至2016年5月14日领取的优惠券，训练集是2016年1月1日至2016年4月13日使用或领取的优惠券。\n",
    "    \n",
    "    2. 测试集是2016年5月15日至2016年6月15日领取的优惠券，训练集是2016年2月1日至2016年5月14日使用或领取的优惠券。\n",
    "    \n",
    "    3. 测试集是2016年7月1日至2016年7月31日领取的优惠券，训练集是2016年3月15日至2016年6月30日使用或领取的优惠券。\n",
    "#### 特征工程，五个方面的特征\n",
    "    - other features        \n",
    "    - coupon related features\n",
    "    - merchant related features\n",
    "    - user related feature\n",
    "    - User_merchant related features\n",
    "\n",
    "### 二、 机器学习 -- 梯度决策提升树gbdt\n",
    "- Gbdt是一种基于boosting思想的集成学习方法，通过多轮迭代，每轮学习一个决策树来拟合之前所有树的预测结果与真实值的残差，最终将所有的回归树加权求和得到最终的模型。\n",
    "- 优点：预测阶段速度快，树与树之间可并行化计算，在分布稠密的数据集上，泛化能力和表达能力都很好……\n",
    "- 为什么选这个模型？网上调研了别人的结果，对比了LR、SVM、RF、XGBoost等多模型后，最终选择了gbdt\n",
    "\n",
    "#### 调参：\n",
    "\n",
    "- **主要参数有三类：**\n",
    "\n",
    "    - 树的数量和大小：包括 n_estimators （树的个数）、max_depth（树的最大深度）、max_leaf_nodes（树的最大叶子节点数）、min_samples_split（树的最小分割样本数）、min_samples_leaf（树的最小叶子节点样本数）\n",
    "    - 学习率和正则化：包括learning_rate（学习率）、subsample（子采样比例）、alpha（正则化系数）\n",
    "    - 损失函数和评价指标：包括loss（损失函数）、scoring（评价指标）\n",
    "    \n",
    "- **调参顺序**\n",
    "    1. n_estimators & learning rate\n",
    "    2. max_depth\n",
    "    3. min_samples_split & min_sample_leaf\n",
    "    4. max_features\n",
    "    5. subsample\n",
    "    \n",
    " （首先选择一个较高的学习率（learning_rate），比如0.1，这样可以加快收敛的速度，然后用GridSearchCV或其他方法来确定一个合适的树的个数（n_estimators），即在验证集上达到最佳性能的迭代次数。\n",
    " \n",
    "接着固定学习率、树的个数和树的最大深度，调整内部节点再划分所需最小样本数（min_samples_split）和叶子节点最少样本数（min_samples_leaf），这些参数影响了树的生长和剪枝，可以防止树过度分裂和过拟合。一般来说，这些参数越大，树越简单，但也不能太大，否则会欠拟合。\n",
    "\n",
    "调整划分时考虑的最大特征数（max_features），这个参数影响了特征的选择和方差的减少。一般来说，如果特征数非常多，我们可以灵活使用其他取值来控制划分时考虑的最大特征数，以控制决策树的生成时间和方差。\n",
    "\n",
    "最后固定前面的参数，调整子采样比例（subsample），这个参数影响了训练集的使用和方差的减少。一般来说，如果取值小于1，则只有一部分样本会去做GBDT的决策树拟合，这样可以减少方差，即防止过拟合，但是会增加样本拟合的偏差，因此取值不能太低，一般在 [0.5, 0.8] 之间。）\n",
    "    \n",
    " ![image.png](attachment:92e3232b-0d79-4ce6-bb23-eb56673cc9d0.png)\n",
    " ![image.png](attachment:5533589b-f97c-4192-a29e-6c4411c2b099.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预处理\n",
    "\n",
    "Features:\n",
    "1. other features（张建民\n",
    "2. Coupon related features（吉政宇\n",
    "3. Merchant related features （梁宇\n",
    "4. User related feature （张子航\n",
    "5. User_merchant related features （曾天一\n",
    "\n",
    "按时间差分划分数据：https://blog.csdn.net/qq_51866108/article/details/129768286\n",
    "\n",
    "「在实际问题中，我们的做法一般是根据历史数据去预测未来某段时间发生的事情，在这种情况下，基于时间窗口的训练集、测试集划分方案就很有用。我们根据线上线下一致性原则，将用户的历史数据按照时间窗口划分，例如选取4月到5月的数据为训练集，5月到6月的数据为测试集。一般在划分时分为标签窗口用于对待考察样本打标签，特征提取窗口用于对待考察样本提取特征。时间窗口划分法中的两个主要概念为窗口时间粒度的大小和窗口滑动的范围，粒度大小指包含了多少天，滑动的范围指从哪一天到哪一天。」\n",
    "\n",
    "具体预处理：\n",
    "1. copy函数主要用于复制对象。\n",
    "2. fillna函数用于填充缺失值。本任务将距离缺失值填充为-1。\n",
    "3. pd.to_datetime函数用于将str类型转为datetime类型。本任务将领券时间、消费时间转为datetime类型。\n",
    "4. 将折扣券转为折扣率。\n",
    "5. 为数据打上标签，将领券后15天被消费的数据标记为正例，其余标记为负例。\n",
    "6. 添加几列新数据：是否为满减优惠券、领券时间为周几、领券月份、消费月份。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:21:12.206138Z",
     "start_time": "2023-12-28T00:21:05.005836Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import date\n",
    "\n",
    "# 数据导入\n",
    "off_train = pd.read_csv('data/ccf_offline_stage1_train.csv')\n",
    "off_train.columns = ['user_id','merchant_id','coupon_id','discount_rate','distance','date_received','date']\n",
    "\n",
    "off_test = pd.read_csv('data/ccf_offline_stage1_test_revised.csv')\n",
    "off_test.columns = ['user_id','merchant_id','coupon_id','discount_rate','distance','date_received']\n",
    "\n",
    "on_train = pd.read_csv('data/ccf_online_stage1_train.csv')\n",
    "on_train.columns = ['user_id','merchant_id','action','coupon_id','discount_rate','date_received','date']\n",
    "\n",
    "# 日期格式处理\n",
    "off_train['date']= off_train['date'].fillna('null')\n",
    "off_train['date_received']= off_train['date_received'].fillna('null')\n",
    "off_train.date= off_train.date.astype('str')\n",
    "off_train.date_received= off_train.date_received.astype('str')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:21:26.123099Z",
     "start_time": "2023-12-28T00:21:24.365813Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# 时间差分 3个预测模型\n",
    "dataset3 = off_test\n",
    "# 0315 0630\n",
    "feature3 = off_train[((off_train.date>='20160315')\n",
    "                      &(off_train.date<='20160630'))\n",
    "                     |((off_train.date=='null')\n",
    "                      &(off_train.date_received>='20160315')\n",
    "                      &(off_train.date_received<='20160630'))]\n",
    "\n",
    "# 0515 0615\n",
    "dataset2 = off_train[(off_train.date_received>='20160515')\n",
    "                     &(off_train.date_received<='20160615')]\n",
    "# 0201 0514\n",
    "feature2 = off_train[(off_train.date>='20160201')\n",
    "                     &(off_train.date<='20160514')\n",
    "                     |((off_train.date=='null')\n",
    "                       &(off_train.date_received>='20160201')\n",
    "                       &(off_train.date_received<='20160514'))]\n",
    "\n",
    "# 0414 0514\n",
    "dataset1 = off_train[(off_train.date_received>='20160414')\n",
    "                     &(off_train.date_received<='20160514')]\n",
    "# 0101 0413\n",
    "feature1 = off_train[(off_train.date>='20160101')\n",
    "                     &(off_train.date<='20160413')\n",
    "                     |((off_train.date=='null')\n",
    "                       &(off_train.date_received>='20160101')\n",
    "                       &(off_train.date_received<='20160413'))]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:25:11.171561Z",
     "start_time": "2023-12-28T00:25:10.786597Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1033243 entries, 1 to 1754883\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count    Dtype  \n",
      "---  ------         --------------    -----  \n",
      " 0   user_id        1033243 non-null  int64  \n",
      " 1   merchant_id    1033243 non-null  int64  \n",
      " 2   coupon_id      537825 non-null   float64\n",
      " 3   discount_rate  537825 non-null   object \n",
      " 4   distance       975790 non-null   float64\n",
      " 5   date_received  1033243 non-null  object \n",
      " 6   date           1033243 non-null  object \n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 63.1+ MB\n"
     ]
    }
   ],
   "source": [
    "feature3.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## other features:\n",
    "      - this_month_user_receive_all_coupon_count\n",
    "      - this_month_user_receive_same_coupon_count\n",
    "      - this_month_user_receive_same_coupon_lastone\n",
    "      - this_month_user_receive_same_coupon_firstone\n",
    "      - this_day_user_receive_all_coupon_count\n",
    "      - this_day_user_receive_same_coupon_count\n",
    "      - day_gap_before, day_gap_after  (receive the same coupon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dataset3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:32:30.444067Z",
     "start_time": "2023-12-28T00:32:30.401236Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/4108828253.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t['this_month_user_receive_all_coupon_count'] = 1\n"
     ]
    }
   ],
   "source": [
    "# 按照用户id 进行重组统计其获得优惠券的数目\n",
    "t = dataset3[['user_id']]\n",
    "t['this_month_user_receive_all_coupon_count'] = 1\n",
    "t = t.groupby('user_id').agg('sum').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:33:42.252906Z",
     "start_time": "2023-12-28T00:33:42.236494Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
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      "text/plain": [
       "   user_id  this_month_user_receive_all_coupon_count\n",
       "0      209                                         2\n",
       "1      215                                         1\n",
       "2      316                                         1\n",
       "3      417                                         1\n",
       "4      432                                         1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:33:53.766358Z",
     "start_time": "2023-12-28T00:33:53.696834Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2370421598.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t1['this_month_user_receive_same_coupon_count'] = 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th></th>\n",
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       "      <th>coupon_id</th>\n",
       "      <th>this_month_user_receive_same_coupon_count</th>\n",
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      ],
      "text/plain": [
       "   user_id  coupon_id  this_month_user_receive_same_coupon_count\n",
       "0      209        825                                          1\n",
       "1      209       7557                                          1\n",
       "2      215       5488                                          1\n",
       "3      316       3992                                          1\n",
       "4      417      12465                                          1"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照用户id和优惠券id(类型) 进行重组统计齐获得相同优惠券的数目\n",
    "t1 = dataset3[['user_id','coupon_id']]\n",
    "t1['this_month_user_receive_same_coupon_count'] = 1\n",
    "t1 = t1.groupby(['user_id','coupon_id']).agg('sum').reset_index()\n",
    "t1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 按照用户id和优惠券id(类型) 进行重组统计齐获得相同优惠券的数目\n",
    "t1 = dataset3[['user_id','coupon_id']]\n",
    "t1['this_month_user_receive_same_coupon_count'] = 1\n",
    "t1 = t1.groupby(['user_id','coupon_id']).agg('sum').reset_index()\n",
    "t1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:34:39.316465Z",
     "start_time": "2023-12-28T00:34:38.078583Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3907088221.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t2.date_received = t2.date_received.astype('str')\n"
     ]
    }
   ],
   "source": [
    "#按照两个id 对收到优惠券的时间统计并按照：隔开\n",
    "t2 = dataset3[['user_id','coupon_id','date_received']]\n",
    "t2.date_received = t2.date_received.astype('str')\n",
    "t2 = t2.groupby(['user_id','coupon_id'])['date_received'].agg(lambda x:':'.join(x)).reset_index()\n",
    "#统计收到的优惠券数量\n",
    "t2['receive_number'] = t2.date_received.apply(lambda s:len(s.split(':')))\n",
    "#只要发了2张及以上的数据\n",
    "t2 = t2[t2.receive_number>1]\n",
    "#添加最早和最晚获取优惠券的时间数据  并只取这两个数据和两个id作为t2\n",
    "t2['max_date_received'] = t2.date_received.apply(lambda s:max([int(float(d)) for d in s.split(':')]))\n",
    "t2['min_date_received'] = t2.date_received.apply(lambda s:min([int(float(d)) for d in s.split(':')]))\n",
    "t2 = t2[['user_id','coupon_id','max_date_received','min_date_received']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:34:43.193622Z",
     "start_time": "2023-12-28T00:34:43.178566Z"
    }
   },
   "outputs": [
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       "      <td>11873</td>\n",
       "      <td>20160730</td>\n",
       "      <td>20160715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  coupon_id  max_date_received  min_date_received\n",
       "6        448      10927           20160710           20160704\n",
       "13       736       3686           20160724           20160713\n",
       "24      1318      11894           20160728           20160720\n",
       "128     9208       6335           20160716           20160703\n",
       "155    11170      11873           20160730           20160715"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:35:16.749021Z",
     "start_time": "2023-12-28T00:35:16.631572Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# t2按照两个id\n",
    "t3 = dataset3[['user_id','coupon_id','date_received']]\n",
    "t3 = pd.merge(t3,t2,on=['user_id','coupon_id'],how='left')\n",
    "\n",
    "t3.max_date_received=t3.max_date_received.astype('float')\n",
    "t3.date_received=t3.date_received.astype('float')\n",
    "t3['this_month_user_receive_same_coupon_lastone'] = t3.max_date_received - t3.date_received\n",
    "t3['this_month_user_receive_same_coupon_firstone'] = t3.date_received - t3.min_date_received\n",
    "def is_firstlastone(x):\n",
    "    if x==0:\n",
    "        return 1\n",
    "    elif x>0:\n",
    "        return 0\n",
    "    else:\n",
    "        return -1 #those only receive once\n",
    "\n",
    "t3.this_month_user_receive_same_coupon_lastone = t3.this_month_user_receive_same_coupon_lastone.apply(is_firstlastone)\n",
    "t3.this_month_user_receive_same_coupon_firstone = t3.this_month_user_receive_same_coupon_firstone.apply(is_firstlastone)\n",
    "t3 = t3[['user_id','coupon_id','date_received','this_month_user_receive_same_coupon_lastone','this_month_user_receive_same_coupon_firstone']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:36:38.049958Z",
     "start_time": "2023-12-28T00:36:38.038322Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>coupon_id</th>\n",
       "      <th>date_received</th>\n",
       "      <th>this_month_user_receive_same_coupon_lastone</th>\n",
       "      <th>this_month_user_receive_same_coupon_firstone</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>5568010</td>\n",
       "      <td>8596</td>\n",
       "      <td>20160722.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5568010</td>\n",
       "      <td>8596</td>\n",
       "      <td>20160722.0</td>\n",
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       "      <th>95</th>\n",
       "      <td>3644167</td>\n",
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       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>3704071</td>\n",
       "      <td>10110</td>\n",
       "      <td>20160707.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>3093769</td>\n",
       "      <td>8306</td>\n",
       "      <td>20160728.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  coupon_id  date_received  \\\n",
       "79   5568010       8596     20160722.0   \n",
       "80   5568010       8596     20160722.0   \n",
       "95   3644167      11463     20160706.0   \n",
       "121  3704071      10110     20160707.0   \n",
       "132  3093769       8306     20160728.0   \n",
       "\n",
       "     this_month_user_receive_same_coupon_lastone  \\\n",
       "79                                             1   \n",
       "80                                             1   \n",
       "95                                             0   \n",
       "121                                            0   \n",
       "132                                            1   \n",
       "\n",
       "     this_month_user_receive_same_coupon_firstone  \n",
       "79                                              1  \n",
       "80                                              1  \n",
       "95                                              1  \n",
       "121                                             1  \n",
       "132                                             1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3[t3['this_month_user_receive_same_coupon_firstone']==1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:37:25.990803Z",
     "start_time": "2023-12-28T00:37:25.925653Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3674352070.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t4['this_day_user_receive_all_coupon_count'] = 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <td>316</td>\n",
       "      <td>20160721</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>20160712</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>432</td>\n",
       "      <td>20160706</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  date_received  this_day_user_receive_all_coupon_count\n",
       "0      209       20160721                                       2\n",
       "1      215       20160703                                       1\n",
       "2      316       20160721                                       1\n",
       "3      417       20160712                                       1\n",
       "4      432       20160706                                       1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t4 = dataset3[['user_id','date_received']]\n",
    "t4['this_day_user_receive_all_coupon_count'] = 1\n",
    "t4 = t4.groupby(['user_id','date_received']).agg('sum').reset_index()\n",
    "t4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:38:05.982775Z",
     "start_time": "2023-12-28T00:38:05.921331Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/423093800.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t5['this_day_user_receive_same_coupon_count'] = 1\n"
     ]
    }
   ],
   "source": [
    "t5 = dataset3[['user_id','coupon_id','date_received']]\n",
    "t5['this_day_user_receive_same_coupon_count'] = 1\n",
    "t5 = t5.groupby(['user_id','coupon_id','date_received']).agg('sum').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:38:10.709259Z",
     "start_time": "2023-12-28T00:38:10.690844Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>coupon_id</th>\n",
       "      <th>date_received</th>\n",
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       "      <th>2</th>\n",
       "      <td>215</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>316</td>\n",
       "      <td>3992</td>\n",
       "      <td>20160721</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>417</td>\n",
       "      <td>12465</td>\n",
       "      <td>20160712</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>20160704</td>\n",
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       "      <td>7361024</td>\n",
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       "      <td>20160715</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>112803 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  coupon_id  date_received  \\\n",
       "0           209        825       20160721   \n",
       "1           209       7557       20160721   \n",
       "2           215       5488       20160703   \n",
       "3           316       3992       20160721   \n",
       "4           417      12465       20160712   \n",
       "...         ...        ...            ...   \n",
       "112798  7360785      10418       20160704   \n",
       "112799  7360845      13602       20160716   \n",
       "112800  7360967      13602       20160704   \n",
       "112801  7361024       2978       20160715   \n",
       "112802  7361024      13602       20160715   \n",
       "\n",
       "        this_day_user_receive_same_coupon_count  \n",
       "0                                             1  \n",
       "1                                             1  \n",
       "2                                             1  \n",
       "3                                             1  \n",
       "4                                             1  \n",
       "...                                         ...  \n",
       "112798                                        1  \n",
       "112799                                        1  \n",
       "112800                                        1  \n",
       "112801                                        1  \n",
       "112802                                        1  \n",
       "\n",
       "[112803 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:38:28.304814Z",
     "start_time": "2023-12-28T00:38:27.107173Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1241048892.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t6.date_received = t6.date_received.astype('str')\n"
     ]
    }
   ],
   "source": [
    "t6 = dataset3[['user_id','coupon_id','date_received']]\n",
    "t6.date_received = t6.date_received.astype('str')\n",
    "t6 = t6.groupby(['user_id','coupon_id'])['date_received'].agg(lambda x:':'.join(x)).reset_index()\n",
    "t6.rename(columns={'date_received':'dates'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:38:43.545802Z",
     "start_time": "2023-12-28T00:38:43.529173Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   user_id  coupon_id     dates\n",
       "0      209        825  20160721\n",
       "1      209       7557  20160721\n",
       "2      215       5488  20160703\n",
       "3      316       3992  20160721\n",
       "4      417      12465  20160712"
      ]
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     "execution_count": 22,
     "metadata": {},
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    }
   ],
   "source": [
    "t6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:39:37.116531Z",
     "start_time": "2023-12-28T00:39:37.097022Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def get_day_gap_before(s):\n",
    "    date_received,dates = s.split('-')\n",
    "    dates = dates.split(':')\n",
    "    gaps = []\n",
    "    for d in dates:\n",
    "        this_gap = (date(int(date_received[0:4]),int(date_received[4:6]),int(date_received[6:8]))-date(int(d[0:4]),int(d[4:6]),int(d[6:8]))).days\n",
    "        if this_gap>0:\n",
    "            gaps.append(this_gap)\n",
    "    if len(gaps)==0:\n",
    "        return -1\n",
    "    else:\n",
    "        return min(gaps)\n",
    "\n",
    "def get_day_gap_after(s):\n",
    "    date_received,dates = s.split('-')\n",
    "    dates = dates.split(':')\n",
    "    gaps = []\n",
    "    for d in dates:\n",
    "        this_gap = (date(int(d[0:4]),int(d[4:6]),int(d[6:8]))-date(int(date_received[0:4]),int(date_received[4:6]),int(date_received[6:8]))).days\n",
    "        if this_gap>0:\n",
    "            gaps.append(this_gap)\n",
    "    if len(gaps)==0:\n",
    "        return -1\n",
    "    else:\n",
    "        return min(gaps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:39:49.090709Z",
     "start_time": "2023-12-28T00:39:48.516401Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "t7 = dataset3[['user_id','coupon_id','date_received']]\n",
    "t7 = pd.merge(t7,t6,on=['user_id','coupon_id'],how='left')\n",
    "t7['date_received_date'] = t7.date_received.astype('str') + '-' + t7.dates\n",
    "t7['day_gap_before'] = t7.date_received_date.apply(get_day_gap_before)\n",
    "t7['day_gap_after'] = t7.date_received_date.apply(get_day_gap_after)\n",
    "t7 = t7[['user_id','coupon_id','date_received','day_gap_before','day_gap_after']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:40:05.128511Z",
     "start_time": "2023-12-28T00:40:05.104090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>coupon_id</th>\n",
       "      <th>date_received</th>\n",
       "      <th>day_gap_before</th>\n",
       "      <th>day_gap_after</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4129537</td>\n",
       "      <td>9983</td>\n",
       "      <td>20160712</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6949378</td>\n",
       "      <td>3429</td>\n",
       "      <td>20160706</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2166529</td>\n",
       "      <td>6928</td>\n",
       "      <td>20160727</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2166529</td>\n",
       "      <td>1808</td>\n",
       "      <td>20160727</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6172162</td>\n",
       "      <td>6500</td>\n",
       "      <td>20160708</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  coupon_id  date_received  day_gap_before  day_gap_after\n",
       "0  4129537       9983       20160712              -1             -1\n",
       "1  6949378       3429       20160706              -1             -1\n",
       "2  2166529       6928       20160727              -1             -1\n",
       "3  2166529       1808       20160727              -1             -1\n",
       "4  6172162       6500       20160708              -1             -1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:40:21.876149Z",
     "start_time": "2023-12-28T00:40:21.427309Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(116204, 11)\n"
     ]
    }
   ],
   "source": [
    "other_feature3 = pd.merge(t1,t,on='user_id')\n",
    "other_feature3 = pd.merge(other_feature3,t3,on=['user_id','coupon_id'])\n",
    "other_feature3 = pd.merge(other_feature3,t4,on=['user_id','date_received'])\n",
    "other_feature3 = pd.merge(other_feature3,t5,on=['user_id','coupon_id','date_received'])\n",
    "other_feature3 = pd.merge(other_feature3,t7,on=['user_id','coupon_id','date_received'])\n",
    "other_feature3.to_csv('data/other_feature3.csv',index=None)\n",
    "print (other_feature3.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dataset2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:42:29.845572Z",
     "start_time": "2023-12-28T00:42:21.891038Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2713485484.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t['this_month_user_receive_all_coupon_count'] = 1\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2713485484.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t1['this_month_user_receive_same_coupon_count'] = 1\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2713485484.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t2.date_received = t2.date_received.astype('str')\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2713485484.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t4['this_day_user_receive_all_coupon_count'] = 1\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2713485484.py:40: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t5['this_day_user_receive_same_coupon_count'] = 1\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2713485484.py:44: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t6.date_received = t6.date_received.astype('str')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(258705, 11)\n"
     ]
    }
   ],
   "source": [
    "#for dataset2\n",
    "t = dataset2[['user_id']]\n",
    "t['this_month_user_receive_all_coupon_count'] = 1\n",
    "t = t.groupby('user_id').agg('sum').reset_index()\n",
    "\n",
    "t1 = dataset2[['user_id','coupon_id']]\n",
    "t1['this_month_user_receive_same_coupon_count'] = 1\n",
    "t1 = t1.groupby(['user_id','coupon_id']).agg('sum').reset_index()\n",
    "\n",
    "t2 = dataset2[['user_id','coupon_id','date_received']]\n",
    "t2.date_received = t2.date_received.astype('str')\n",
    "t2 = t2.groupby(['user_id','coupon_id'])['date_received'].agg(lambda x:':'.join(x)).reset_index()\n",
    "t2['receive_number'] = t2.date_received.apply(lambda s:len(s.split(':')))\n",
    "t2 = t2[t2.receive_number>1]\n",
    "t2['max_date_received'] = t2.date_received.apply(lambda s:max([int(float(d)) for d in s.split(':')]))\n",
    "t2['min_date_received'] = t2.date_received.apply(lambda s:min([int(float(d)) for d in s.split(':')]))\n",
    "t2 = t2[['user_id','coupon_id','max_date_received','min_date_received']]\n",
    "\n",
    "t3 = dataset2[['user_id','coupon_id','date_received']]\n",
    "t3 = pd.merge(t3,t2,on=['user_id','coupon_id'],how='left')\n",
    "t3['this_month_user_receive_same_coupon_lastone'] = t3.max_date_received - t3.date_received.astype('float').astype('int')\n",
    "t3['this_month_user_receive_same_coupon_firstone'] = t3.date_received.astype('float').astype('int') - t3.min_date_received\n",
    "def is_firstlastone(x):\n",
    "    if x==0:\n",
    "        return 1\n",
    "    elif x>0:\n",
    "        return 0\n",
    "    else:\n",
    "        return -1 #those only receive once\n",
    "\n",
    "t3.this_month_user_receive_same_coupon_lastone = t3.this_month_user_receive_same_coupon_lastone.apply(is_firstlastone)\n",
    "t3.this_month_user_receive_same_coupon_firstone = t3.this_month_user_receive_same_coupon_firstone.apply(is_firstlastone)\n",
    "t3 = t3[['user_id','coupon_id','date_received','this_month_user_receive_same_coupon_lastone','this_month_user_receive_same_coupon_firstone']]\n",
    "\n",
    "t4 = dataset2[['user_id','date_received']]\n",
    "t4['this_day_user_receive_all_coupon_count'] = 1\n",
    "t4 = t4.groupby(['user_id','date_received']).agg('sum').reset_index()\n",
    "\n",
    "t5 = dataset2[['user_id','coupon_id','date_received']]\n",
    "t5['this_day_user_receive_same_coupon_count'] = 1\n",
    "t5 = t5.groupby(['user_id','coupon_id','date_received']).agg('sum').reset_index()\n",
    "\n",
    "t6 = dataset2[['user_id','coupon_id','date_received']]\n",
    "t6.date_received = t6.date_received.astype('str')\n",
    "t6 = t6.groupby(['user_id','coupon_id'])['date_received'].agg(lambda x:':'.join(x)).reset_index()\n",
    "t6.rename(columns={'date_received':'dates'},inplace=True)\n",
    "\n",
    "def get_day_gap_before(s):\n",
    "    date_received,dates = s.split('-')\n",
    "    dates = dates.split(':')\n",
    "    gaps = []\n",
    "    for d in dates:\n",
    "        this_gap = (date(int(date_received[0:4]),int(date_received[4:6]),int(date_received[6:8]))-date(int(d[0:4]),int(d[4:6]),int(d[6:8]))).days\n",
    "        if this_gap>0:\n",
    "            gaps.append(this_gap)\n",
    "    if len(gaps)==0:\n",
    "        return -1\n",
    "    else:\n",
    "        return min(gaps)\n",
    "\n",
    "def get_day_gap_after(s):\n",
    "    date_received,dates = s.split('-')\n",
    "    dates = dates.split(':')\n",
    "    gaps = []\n",
    "    for d in dates:\n",
    "        this_gap = (date(int(d[0:4]),int(d[4:6]),int(d[6:8]))-date(int(date_received[0:4]),int(date_received[4:6]),int(date_received[6:8]))).days\n",
    "        if this_gap>0:\n",
    "            gaps.append(this_gap)\n",
    "    if len(gaps)==0:\n",
    "        return -1\n",
    "    else:\n",
    "        return min(gaps)\n",
    "\n",
    "\n",
    "t7 = dataset2[['user_id','coupon_id','date_received']]\n",
    "t7 = pd.merge(t7,t6,on=['user_id','coupon_id'],how='left')\n",
    "t7['date_received_date'] = t7.date_received.astype('str') + '-' + t7.dates\n",
    "t7['day_gap_before'] = t7.date_received_date.apply(get_day_gap_before)\n",
    "t7['day_gap_after'] = t7.date_received_date.apply(get_day_gap_after)\n",
    "t7 = t7[['user_id','coupon_id','date_received','day_gap_before','day_gap_after']]\n",
    "\n",
    "other_feature2 = pd.merge(t1,t,on='user_id')\n",
    "other_feature2 = pd.merge(other_feature2,t3,on=['user_id','coupon_id'])\n",
    "other_feature2 = pd.merge(other_feature2,t4,on=['user_id','date_received'])\n",
    "other_feature2 = pd.merge(other_feature2,t5,on=['user_id','coupon_id','date_received'])\n",
    "other_feature2 = pd.merge(other_feature2,t7,on=['user_id','coupon_id','date_received'])\n",
    "other_feature2.to_csv('data/other_feature2.csv',index=None)\n",
    "print (other_feature2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dataset1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:43:09.925775Z",
     "start_time": "2023-12-28T00:43:05.728753Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1047233047.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t['this_month_user_receive_all_coupon_count'] = 1\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1047233047.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t1['this_month_user_receive_same_coupon_count'] = 1\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1047233047.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t2.date_received = t2.date_received.astype('str')\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1047233047.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t4['this_day_user_receive_all_coupon_count'] = 1\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1047233047.py:40: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t5['this_day_user_receive_same_coupon_count'] = 1\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1047233047.py:44: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t6.date_received = t6.date_received.astype('str')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(134232, 11)\n"
     ]
    }
   ],
   "source": [
    "#for dataset1\n",
    "t = dataset1[['user_id']]\n",
    "t['this_month_user_receive_all_coupon_count'] = 1\n",
    "t = t.groupby('user_id').agg('sum').reset_index()\n",
    "\n",
    "t1 = dataset1[['user_id','coupon_id']]\n",
    "t1['this_month_user_receive_same_coupon_count'] = 1\n",
    "t1 = t1.groupby(['user_id','coupon_id']).agg('sum').reset_index()\n",
    "\n",
    "t2 = dataset1[['user_id','coupon_id','date_received']]\n",
    "t2.date_received = t2.date_received.astype('str')\n",
    "t2 = t2.groupby(['user_id','coupon_id'])['date_received'].agg(lambda x:':'.join(x)).reset_index()\n",
    "t2['receive_number'] = t2.date_received.apply(lambda s:len(s.split(':')))\n",
    "t2 = t2[t2.receive_number>1]\n",
    "t2['max_date_received'] = t2.date_received.apply(lambda s:max([int(float(d)) for d in s.split(':')]))\n",
    "t2['min_date_received'] = t2.date_received.apply(lambda s:min([int(float(d)) for d in s.split(':')]))\n",
    "t2 = t2[['user_id','coupon_id','max_date_received','min_date_received']]\n",
    "\n",
    "t3 = dataset1[['user_id','coupon_id','date_received']]\n",
    "t3 = pd.merge(t3,t2,on=['user_id','coupon_id'],how='left')\n",
    "t3['this_month_user_receive_same_coupon_lastone'] = t3.max_date_received - t3.date_received.astype('float').astype('int')\n",
    "t3['this_month_user_receive_same_coupon_firstone'] = t3.date_received.astype('float').astype('int') - t3.min_date_received\n",
    "def is_firstlastone(x):\n",
    "    if x==0:\n",
    "        return 1\n",
    "    elif x>0:\n",
    "        return 0\n",
    "    else:\n",
    "        return -1 #those only receive once\n",
    "\n",
    "t3.this_month_user_receive_same_coupon_lastone = t3.this_month_user_receive_same_coupon_lastone.apply(is_firstlastone)\n",
    "t3.this_month_user_receive_same_coupon_firstone = t3.this_month_user_receive_same_coupon_firstone.apply(is_firstlastone)\n",
    "t3 = t3[['user_id','coupon_id','date_received','this_month_user_receive_same_coupon_lastone','this_month_user_receive_same_coupon_firstone']]\n",
    "\n",
    "t4 = dataset1[['user_id','date_received']]\n",
    "t4['this_day_user_receive_all_coupon_count'] = 1\n",
    "t4 = t4.groupby(['user_id','date_received']).agg('sum').reset_index()\n",
    "\n",
    "t5 = dataset1[['user_id','coupon_id','date_received']]\n",
    "t5['this_day_user_receive_same_coupon_count'] = 1\n",
    "t5 = t5.groupby(['user_id','coupon_id','date_received']).agg('sum').reset_index()\n",
    "\n",
    "t6 = dataset1[['user_id','coupon_id','date_received']]\n",
    "t6.date_received = t6.date_received.astype('str')\n",
    "t6 = t6.groupby(['user_id','coupon_id'])['date_received'].agg(lambda x:':'.join(x)).reset_index()\n",
    "t6.rename(columns={'date_received':'dates'},inplace=True)\n",
    "\n",
    "def get_day_gap_before(s):\n",
    "    date_received,dates = s.split('-')\n",
    "    dates = dates.split(':')\n",
    "    gaps = []\n",
    "    for d in dates:\n",
    "        this_gap = (date(int(date_received[0:4]),int(date_received[4:6]),int(date_received[6:8]))-date(int(d[0:4]),int(d[4:6]),int(d[6:8]))).days\n",
    "        if this_gap>0:\n",
    "            gaps.append(this_gap)\n",
    "    if len(gaps)==0:\n",
    "        return -1\n",
    "    else:\n",
    "        return min(gaps)\n",
    "\n",
    "def get_day_gap_after(s):\n",
    "    date_received,dates = s.split('-')\n",
    "    dates = dates.split(':')\n",
    "    gaps = []\n",
    "    for d in dates:\n",
    "        this_gap = (date(int(d[0:4]),int(d[4:6]),int(d[6:8]))-date(int(date_received[0:4]),int(date_received[4:6]),int(date_received[6:8]))).days\n",
    "        if this_gap>0:\n",
    "            gaps.append(this_gap)\n",
    "    if len(gaps)==0:\n",
    "        return -1\n",
    "    else:\n",
    "        return min(gaps)\n",
    "\n",
    "\n",
    "t7 = dataset1[['user_id','coupon_id','date_received']]\n",
    "t7 = pd.merge(t7,t6,on=['user_id','coupon_id'],how='left')\n",
    "t7['date_received_date'] = t7.date_received.astype('str') + '-' + t7.dates\n",
    "t7['day_gap_before'] = t7.date_received_date.apply(get_day_gap_before)\n",
    "t7['day_gap_after'] = t7.date_received_date.apply(get_day_gap_after)\n",
    "t7 = t7[['user_id','coupon_id','date_received','day_gap_before','day_gap_after']]\n",
    "\n",
    "other_feature1 = pd.merge(t1,t,on='user_id')\n",
    "other_feature1 = pd.merge(other_feature1,t3,on=['user_id','coupon_id'])\n",
    "other_feature1 = pd.merge(other_feature1,t4,on=['user_id','date_received'])\n",
    "other_feature1 = pd.merge(other_feature1,t5,on=['user_id','coupon_id','date_received'])\n",
    "other_feature1 = pd.merge(other_feature1,t7,on=['user_id','coupon_id','date_received'])\n",
    "other_feature1.to_csv('data/other_feature1.csv',index=None)\n",
    "print (other_feature1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coupon related features\n",
    "- discount_rate\n",
    "- discount_man\n",
    "- discount_jian\n",
    "- is_man_jian\n",
    "- day_of_week\n",
    "- day_of_month. (date_received)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:45:37.074925Z",
     "start_time": "2023-12-28T00:45:37.059344Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def calc_discount_rate(s):\n",
    "    s =str(s)\n",
    "    s = s.split(':')\n",
    "    if len(s)==1:\n",
    "        return float(s[0])\n",
    "    else:\n",
    "        return 1.0-float(s[1])/float(s[0])\n",
    "\n",
    "def get_discount_man(s):\n",
    "    s =str(s)\n",
    "    s = s.split(':')\n",
    "    if len(s)==1:\n",
    "        return 'null'\n",
    "    else:\n",
    "        return int(s[0])\n",
    "\n",
    "def get_discount_jian(s):\n",
    "    s =str(s)\n",
    "    s = s.split(':')\n",
    "    if len(s)==1:\n",
    "        return 'null'\n",
    "    else:\n",
    "        return int(s[1])\n",
    "\n",
    "def is_man_jian(s):\n",
    "    s =str(s)\n",
    "    s = s.split(':')\n",
    "    if len(s)==1:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:45:47.532025Z",
     "start_time": "2023-12-28T00:45:46.527518Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1360801462.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  d['coupon_count'] = 1\n"
     ]
    }
   ],
   "source": [
    "#dataset3\n",
    "dataset3['day_of_week'] = dataset3.date_received.astype('str').apply(lambda x:date(int(x[0:4]),int(x[4:6]),int(x[6:8])).weekday()+1)\n",
    "dataset3['day_of_month'] = dataset3.date_received.astype('str').apply(lambda x:int(x[6:8]))\n",
    "dataset3['days_distance'] = dataset3.date_received.astype('str').apply(lambda x:(date(int(x[0:4]),int(x[4:6]),int(x[6:8]))-date(2016,6,30)).days)\n",
    "dataset3['discount_man'] = dataset3.discount_rate.apply(get_discount_man)\n",
    "dataset3['discount_jian'] = dataset3.discount_rate.apply(get_discount_jian)\n",
    "dataset3['is_man_jian'] = dataset3.discount_rate.apply(is_man_jian)\n",
    "dataset3['discount_rate'] = dataset3.discount_rate.apply(calc_discount_rate)\n",
    "d = dataset3[['coupon_id']]\n",
    "d['coupon_count'] = 1\n",
    "d = d.groupby('coupon_id').agg('sum').reset_index()\n",
    "dataset3 = pd.merge(dataset3,d,on='coupon_id',how='left')\n",
    "dataset3.to_csv('data/coupon3_feature.csv',index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:45:57.732782Z",
     "start_time": "2023-12-28T00:45:55.427775Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1406818795.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset2['day_of_week'] = dataset2.date_received.astype('str').apply(lambda x:date(int(x[0:4]),int(x[4:6]),int(x[6:8])).weekday()+1)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1406818795.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset2['day_of_month'] = dataset2.date_received.astype('str').apply(lambda x:int(x[6:8]))\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1406818795.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset2['days_distance'] = dataset2.date_received.astype('str').apply(lambda x:(date(int(x[0:4]),int(x[4:6]),int(x[6:8]))-date(2016,5,14)).days)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1406818795.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset2['discount_man'] = dataset2.discount_rate.apply(get_discount_man)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1406818795.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset2['discount_jian'] = dataset2.discount_rate.apply(get_discount_jian)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1406818795.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset2['is_man_jian'] = dataset2.discount_rate.apply(is_man_jian)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1406818795.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset2['discount_rate'] = dataset2.discount_rate.apply(calc_discount_rate)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1406818795.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  d['coupon_count'] = 1\n"
     ]
    }
   ],
   "source": [
    "#dataset2\n",
    "dataset2['day_of_week'] = dataset2.date_received.astype('str').apply(lambda x:date(int(x[0:4]),int(x[4:6]),int(x[6:8])).weekday()+1)\n",
    "dataset2['day_of_month'] = dataset2.date_received.astype('str').apply(lambda x:int(x[6:8]))\n",
    "dataset2['days_distance'] = dataset2.date_received.astype('str').apply(lambda x:(date(int(x[0:4]),int(x[4:6]),int(x[6:8]))-date(2016,5,14)).days)\n",
    "dataset2['discount_man'] = dataset2.discount_rate.apply(get_discount_man)\n",
    "dataset2['discount_jian'] = dataset2.discount_rate.apply(get_discount_jian)\n",
    "dataset2['is_man_jian'] = dataset2.discount_rate.apply(is_man_jian)\n",
    "dataset2['discount_rate'] = dataset2.discount_rate.apply(calc_discount_rate)\n",
    "d = dataset2[['coupon_id']]\n",
    "d['coupon_count'] = 1\n",
    "d = d.groupby('coupon_id').agg('sum').reset_index()\n",
    "dataset2 = pd.merge(dataset2,d,on='coupon_id',how='left')\n",
    "dataset2.to_csv('data/coupon2_feature.csv',index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:46:05.346006Z",
     "start_time": "2023-12-28T00:46:04.152750Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/360052370.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset1['day_of_week'] = dataset1.date_received.astype('str').apply(lambda x:date(int(x[0:4]),int(x[4:6]),int(x[6:8])).weekday()+1)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/360052370.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset1['day_of_month'] = dataset1.date_received.astype('str').apply(lambda x:int(x[6:8]))\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/360052370.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset1['days_distance'] = dataset1.date_received.astype('str').apply(lambda x:(date(int(x[0:4]),int(x[4:6]),int(x[6:8]))-date(2016,4,13)).days)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/360052370.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset1['discount_man'] = dataset1.discount_rate.apply(get_discount_man)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/360052370.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset1['discount_jian'] = dataset1.discount_rate.apply(get_discount_jian)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/360052370.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset1['is_man_jian'] = dataset1.discount_rate.apply(is_man_jian)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/360052370.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset1['discount_rate'] = dataset1.discount_rate.apply(calc_discount_rate)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/360052370.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  d['coupon_count'] = 1\n"
     ]
    }
   ],
   "source": [
    "#dataset1\n",
    "dataset1['day_of_week'] = dataset1.date_received.astype('str').apply(lambda x:date(int(x[0:4]),int(x[4:6]),int(x[6:8])).weekday()+1)\n",
    "dataset1['day_of_month'] = dataset1.date_received.astype('str').apply(lambda x:int(x[6:8]))\n",
    "dataset1['days_distance'] = dataset1.date_received.astype('str').apply(lambda x:(date(int(x[0:4]),int(x[4:6]),int(x[6:8]))-date(2016,4,13)).days)\n",
    "dataset1['discount_man'] = dataset1.discount_rate.apply(get_discount_man)\n",
    "dataset1['discount_jian'] = dataset1.discount_rate.apply(get_discount_jian)\n",
    "dataset1['is_man_jian'] = dataset1.discount_rate.apply(is_man_jian)\n",
    "dataset1['discount_rate'] = dataset1.discount_rate.apply(calc_discount_rate)\n",
    "d = dataset1[['coupon_id']]\n",
    "d['coupon_count'] = 1\n",
    "d = d.groupby('coupon_id').agg('sum').reset_index()\n",
    "dataset1 = pd.merge(dataset1,d,on='coupon_id',how='left')\n",
    "dataset1.to_csv('data/coupon1_feature.csv',index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merchant related features\n",
    "total_sales. sales_use_coupon.  total_coupon\n",
    "\n",
    "coupon_rate = sales_use_coupon/total_sales.\n",
    "\n",
    "transfer_rate = sales_use_coupon/total_coupon.\n",
    "\n",
    "merchant_avg_distance,\n",
    "\n",
    "merchant_min_distance,\n",
    "\n",
    "merchant_max_distance of those use coupon\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:46:57.056240Z",
     "start_time": "2023-12-28T00:46:56.435689Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3362231561.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t.drop_duplicates(inplace=True)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3362231561.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  merchant3.coupon_id=merchant3.coupon_id.replace(np.nan,'null')\n"
     ]
    }
   ],
   "source": [
    "#for dataset3\n",
    "merchant3 = feature3[['merchant_id','coupon_id','distance','date_received','date']]\n",
    "\n",
    "t = merchant3[['merchant_id']]\n",
    "t.drop_duplicates(inplace=True)\n",
    "\n",
    "t1 = merchant3[merchant3.date!='null'][['merchant_id']]\n",
    "t1['total_sales'] = 1\n",
    "t1 = t1.groupby('merchant_id').agg('sum').reset_index()\n",
    "\n",
    "merchant3.coupon_id=merchant3.coupon_id.replace(np.nan,'null')\n",
    "t2 = merchant3[(merchant3.date!='null')&(merchant3.coupon_id!='null')][['merchant_id']]\n",
    "t2['sales_use_coupon'] = 1\n",
    "t2 = t2.groupby('merchant_id').agg('sum').reset_index()\n",
    "\n",
    "t3 = merchant3[merchant3.coupon_id!='null'][['merchant_id']]\n",
    "t3['total_coupon'] = 1\n",
    "t3 = t3.groupby('merchant_id').agg('sum').reset_index()\n",
    "\n",
    "t4 = merchant3[(merchant3.date!='null')&(merchant3.coupon_id!='null')][['merchant_id','distance']]\n",
    "t4.replace('null',-1,inplace=True)\n",
    "#t4.distance = t4.distance.astype('int')\n",
    "t4.replace(-1,np.nan,inplace=True)\n",
    "t5 = t4.groupby('merchant_id').agg('min').reset_index()\n",
    "t5.rename(columns={'distance':'merchant_min_distance'},inplace=True)\n",
    "\n",
    "t6 = t4.groupby('merchant_id').agg('max').reset_index()\n",
    "t6.rename(columns={'distance':'merchant_max_distance'},inplace=True)\n",
    "\n",
    "t7 = t4.groupby('merchant_id').agg('mean').reset_index()\n",
    "t7.rename(columns={'distance':'merchant_mean_distance'},inplace=True)\n",
    "\n",
    "t8 = t4.groupby('merchant_id').agg('median').reset_index()\n",
    "t8.rename(columns={'distance':'merchant_median_distance'},inplace=True)\n",
    "\n",
    "merchant3_feature = pd.merge(t,t1,on='merchant_id',how='left')\n",
    "merchant3_feature = pd.merge(merchant3_feature,t2,on='merchant_id',how='left')\n",
    "merchant3_feature = pd.merge(merchant3_feature,t3,on='merchant_id',how='left')\n",
    "merchant3_feature = pd.merge(merchant3_feature,t5,on='merchant_id',how='left')\n",
    "merchant3_feature = pd.merge(merchant3_feature,t6,on='merchant_id',how='left')\n",
    "merchant3_feature = pd.merge(merchant3_feature,t7,on='merchant_id',how='left')\n",
    "merchant3_feature = pd.merge(merchant3_feature,t8,on='merchant_id',how='left')\n",
    "merchant3_feature.sales_use_coupon = merchant3_feature.sales_use_coupon.replace(np.nan,0) #fillna with 0\n",
    "merchant3_feature['merchant_coupon_transfer_rate'] = merchant3_feature.sales_use_coupon.astype('float') / merchant3_feature.total_coupon\n",
    "merchant3_feature['coupon_rate'] = merchant3_feature.sales_use_coupon.astype('float') / merchant3_feature.total_sales\n",
    "merchant3_feature.total_coupon = merchant3_feature.total_coupon.replace(np.nan,0) #fillna with 0\n",
    "merchant3_feature.to_csv('data/merchant3_feature.csv',index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:47:22.424276Z",
     "start_time": "2023-12-28T00:47:21.977068Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2945266552.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t.drop_duplicates(inplace=True)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2945266552.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  merchant2.coupon_id=merchant2.coupon_id.replace(np.nan,'null')\n"
     ]
    }
   ],
   "source": [
    "#for dataset2\n",
    "merchant2 = feature2[['merchant_id','coupon_id','distance','date_received','date']]\n",
    "\n",
    "t = merchant2[['merchant_id']]\n",
    "t.drop_duplicates(inplace=True)\n",
    "\n",
    "t1 = merchant2[merchant2.date!='null'][['merchant_id']]\n",
    "t1['total_sales'] = 1\n",
    "t1 = t1.groupby('merchant_id').agg('sum').reset_index()\n",
    "\n",
    "merchant2.coupon_id=merchant2.coupon_id.replace(np.nan,'null')\n",
    "t2 = merchant2[(merchant2.date!='null')&(merchant2.coupon_id!='null')][['merchant_id']]\n",
    "t2['sales_use_coupon'] = 1\n",
    "t2 = t2.groupby('merchant_id').agg('sum').reset_index()\n",
    "\n",
    "t3 = merchant2[merchant2.coupon_id!='null'][['merchant_id']]\n",
    "t3['total_coupon'] = 1\n",
    "t3 = t3.groupby('merchant_id').agg('sum').reset_index()\n",
    "\n",
    "t4 = merchant2[(merchant2.date!='null')&(merchant2.coupon_id!='null')][['merchant_id','distance']]\n",
    "t4.replace('null',-1,inplace=True)\n",
    "#t4.distance = t4.distance.astype('int')\n",
    "t4.replace(-1,np.nan,inplace=True)\n",
    "t5 = t4.groupby('merchant_id').agg('min').reset_index()\n",
    "t5.rename(columns={'distance':'merchant_min_distance'},inplace=True)\n",
    "\n",
    "t6 = t4.groupby('merchant_id').agg('max').reset_index()\n",
    "t6.rename(columns={'distance':'merchant_max_distance'},inplace=True)\n",
    "\n",
    "t7 = t4.groupby('merchant_id').agg('mean').reset_index()\n",
    "t7.rename(columns={'distance':'merchant_mean_distance'},inplace=True)\n",
    "\n",
    "t8 = t4.groupby('merchant_id').agg('median').reset_index()\n",
    "t8.rename(columns={'distance':'merchant_median_distance'},inplace=True)\n",
    "\n",
    "merchant2_feature = pd.merge(t,t1,on='merchant_id',how='left')\n",
    "merchant2_feature = pd.merge(merchant2_feature,t2,on='merchant_id',how='left')\n",
    "merchant2_feature = pd.merge(merchant2_feature,t3,on='merchant_id',how='left')\n",
    "merchant2_feature = pd.merge(merchant2_feature,t5,on='merchant_id',how='left')\n",
    "merchant2_feature = pd.merge(merchant2_feature,t6,on='merchant_id',how='left')\n",
    "merchant2_feature = pd.merge(merchant2_feature,t7,on='merchant_id',how='left')\n",
    "merchant2_feature = pd.merge(merchant2_feature,t8,on='merchant_id',how='left')\n",
    "merchant2_feature.sales_use_coupon = merchant2_feature.sales_use_coupon.replace(np.nan,0) #fillna with 0\n",
    "merchant2_feature['merchant_coupon_transfer_rate'] = merchant2_feature.sales_use_coupon.astype('float') / merchant2_feature.total_coupon\n",
    "merchant2_feature['coupon_rate'] = merchant2_feature.sales_use_coupon.astype('float') / merchant2_feature.total_sales\n",
    "merchant2_feature.total_coupon = merchant2_feature.total_coupon.replace(np.nan,0) #fillna with 0\n",
    "merchant2_feature.to_csv('data/merchant2_feature.csv',index=None)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:47:49.365956Z",
     "start_time": "2023-12-28T00:47:48.865068Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3591933675.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t.drop_duplicates(inplace=True)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3591933675.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  merchant1.coupon_id=merchant1.coupon_id.replace(np.nan,'null')\n"
     ]
    }
   ],
   "source": [
    "#for dataset1\n",
    "merchant1 = feature1[['merchant_id','coupon_id','distance','date_received','date']]\n",
    "\n",
    "t = merchant1[['merchant_id']]\n",
    "t.drop_duplicates(inplace=True)\n",
    "\n",
    "t1 = merchant1[merchant1.date!='null'][['merchant_id']]\n",
    "t1['total_sales'] = 1\n",
    "t1 = t1.groupby('merchant_id').agg('sum').reset_index()\n",
    "\n",
    "merchant1.coupon_id=merchant1.coupon_id.replace(np.nan,'null')\n",
    "t2 = merchant1[(merchant1.date!='null')&(merchant1.coupon_id!='null')][['merchant_id']]\n",
    "t2['sales_use_coupon'] = 1\n",
    "t2 = t2.groupby('merchant_id').agg('sum').reset_index()\n",
    "\n",
    "t3 = merchant1[merchant1.coupon_id!='null'][['merchant_id']]\n",
    "t3['total_coupon'] = 1\n",
    "t3 = t3.groupby('merchant_id').agg('sum').reset_index()\n",
    "\n",
    "t4 = merchant1[(merchant1.date!='null')&(merchant1.coupon_id!='null')][['merchant_id','distance']]\n",
    "t4.replace('null',-1,inplace=True)\n",
    "#t4.distance = t4.distance.astype('int')\n",
    "t4.replace(-1,np.nan,inplace=True)\n",
    "t5 = t4.groupby('merchant_id').agg('min').reset_index()\n",
    "t5.rename(columns={'distance':'merchant_min_distance'},inplace=True)\n",
    "\n",
    "t6 = t4.groupby('merchant_id').agg('max').reset_index()\n",
    "t6.rename(columns={'distance':'merchant_max_distance'},inplace=True)\n",
    "\n",
    "t7 = t4.groupby('merchant_id').agg('mean').reset_index()\n",
    "t7.rename(columns={'distance':'merchant_mean_distance'},inplace=True)\n",
    "\n",
    "t8 = t4.groupby('merchant_id').agg('median').reset_index()\n",
    "t8.rename(columns={'distance':'merchant_median_distance'},inplace=True)\n",
    "\n",
    "\n",
    "merchant1_feature = pd.merge(t,t1,on='merchant_id',how='left')\n",
    "merchant1_feature = pd.merge(merchant1_feature,t2,on='merchant_id',how='left')\n",
    "merchant1_feature = pd.merge(merchant1_feature,t3,on='merchant_id',how='left')\n",
    "merchant1_feature = pd.merge(merchant1_feature,t5,on='merchant_id',how='left')\n",
    "merchant1_feature = pd.merge(merchant1_feature,t6,on='merchant_id',how='left')\n",
    "merchant1_feature = pd.merge(merchant1_feature,t7,on='merchant_id',how='left')\n",
    "merchant1_feature = pd.merge(merchant1_feature,t8,on='merchant_id',how='left')\n",
    "merchant1_feature.sales_use_coupon = merchant1_feature.sales_use_coupon.replace(np.nan,0) #fillna with 0\n",
    "merchant1_feature['merchant_coupon_transfer_rate'] = merchant1_feature.sales_use_coupon.astype('float') / merchant1_feature.total_coupon\n",
    "merchant1_feature['coupon_rate'] = merchant1_feature.sales_use_coupon.astype('float') / merchant1_feature.total_sales\n",
    "merchant1_feature.total_coupon = merchant1_feature.total_coupon.replace(np.nan,0) #fillna with 0\n",
    "merchant1_feature.to_csv('data/merchant1_feature.csv',index=None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User related feature\n",
    "count_merchant.\n",
    "\n",
    "user_avg_distance, user_min_distance,user_max_distance.\n",
    "\n",
    "buy_use_coupon. buy_total. coupon_received.\n",
    "\n",
    "buy_use_coupon/coupon_received.\n",
    "\n",
    "buy_use_coupon/buy_total\n",
    "\n",
    "user_date_datereceived_gap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:48:28.274658Z",
     "start_time": "2023-12-28T00:48:28.261110Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def get_user_date_datereceived_gap(s):\n",
    "    s = s.split(':')\n",
    "    return (date(int(s[0][0:4]),int(s[0][4:6]),int(s[0][6:8])) - date(int(s[1][0:4]),int(s[1][4:6]),int(s[1][6:8]))).days\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:48:53.583988Z",
     "start_time": "2023-12-28T00:48:49.981858Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2691282177.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t.drop_duplicates(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "#for dataset3\n",
    "user3 = feature3[['user_id','merchant_id','coupon_id','discount_rate','distance','date_received','date']]\n",
    "\n",
    "t = user3[['user_id']]\n",
    "t.drop_duplicates(inplace=True)\n",
    "\n",
    "t1 = user3[user3.date!='null'][['user_id','merchant_id']]\n",
    "t1.drop_duplicates(inplace=True)\n",
    "t1.merchant_id = 1\n",
    "t1 = t1.groupby('user_id').agg('sum').reset_index()\n",
    "t1.rename(columns={'merchant_id':'count_merchant'},inplace=True)\n",
    "user3['coupon_id']=user3['coupon_id'].replace(np.nan,'null')\n",
    "t2 = user3[(user3.date!='null')&(user3.coupon_id!='null')][['user_id','distance']]\n",
    "t2.replace('null',-1,inplace=True)\n",
    "#t2.distance = t2.distance.astype('int')\n",
    "t2.replace(-1,np.nan,inplace=True)\n",
    "t3 = t2.groupby('user_id').agg('min').reset_index()\n",
    "t3.rename(columns={'distance':'user_min_distance'},inplace=True)\n",
    "\n",
    "t4 = t2.groupby('user_id').agg('max').reset_index()\n",
    "t4.rename(columns={'distance':'user_max_distance'},inplace=True)\n",
    "\n",
    "t5 = t2.groupby('user_id').agg('mean').reset_index()\n",
    "t5.rename(columns={'distance':'user_mean_distance'},inplace=True)\n",
    "\n",
    "t6 = t2.groupby('user_id').agg('median').reset_index()\n",
    "t6.rename(columns={'distance':'user_median_distance'},inplace=True)\n",
    "\n",
    "t7 = user3[(user3.date!='null')&(user3.coupon_id!='null')][['user_id']]\n",
    "t7['buy_use_coupon'] = 1\n",
    "t7 = t7.groupby('user_id').agg('sum').reset_index()\n",
    "\n",
    "t8 = user3[user3.date!='null'][['user_id']]\n",
    "t8['buy_total'] = 1\n",
    "t8 = t8.groupby('user_id').agg('sum').reset_index()\n",
    "\n",
    "t9 = user3[user3.coupon_id!='null'][['user_id']]\n",
    "t9['coupon_received'] = 1\n",
    "t9 = t9.groupby('user_id').agg('sum').reset_index()\n",
    "\n",
    "t10 = user3[(user3.date_received!='null')&(user3.date!='null')][['user_id','date_received','date']]\n",
    "t10['user_date_datereceived_gap'] = t10.date + ':' + t10.date_received\n",
    "t10.user_date_datereceived_gap = t10.user_date_datereceived_gap.apply(get_user_date_datereceived_gap)\n",
    "t10 = t10[['user_id','user_date_datereceived_gap']]\n",
    "\n",
    "t11 = t10.groupby('user_id').agg('mean').reset_index()\n",
    "t11.rename(columns={'user_date_datereceived_gap':'avg_user_date_datereceived_gap'},inplace=True)\n",
    "t12 = t10.groupby('user_id').agg('min').reset_index()\n",
    "t12.rename(columns={'user_date_datereceived_gap':'min_user_date_datereceived_gap'},inplace=True)\n",
    "t13 = t10.groupby('user_id').agg('max').reset_index()\n",
    "t13.rename(columns={'user_date_datereceived_gap':'max_user_date_datereceived_gap'},inplace=True)\n",
    "\n",
    "\n",
    "user3_feature = pd.merge(t,t1,on='user_id',how='left')\n",
    "user3_feature = pd.merge(user3_feature,t3,on='user_id',how='left')\n",
    "user3_feature = pd.merge(user3_feature,t4,on='user_id',how='left')\n",
    "user3_feature = pd.merge(user3_feature,t5,on='user_id',how='left')\n",
    "user3_feature = pd.merge(user3_feature,t6,on='user_id',how='left')\n",
    "user3_feature = pd.merge(user3_feature,t7,on='user_id',how='left')\n",
    "user3_feature = pd.merge(user3_feature,t8,on='user_id',how='left')\n",
    "user3_feature = pd.merge(user3_feature,t9,on='user_id',how='left')\n",
    "user3_feature = pd.merge(user3_feature,t11,on='user_id',how='left')\n",
    "user3_feature = pd.merge(user3_feature,t12,on='user_id',how='left')\n",
    "user3_feature = pd.merge(user3_feature,t13,on='user_id',how='left')\n",
    "user3_feature.count_merchant = user3_feature.count_merchant.replace(np.nan,0)\n",
    "user3_feature.buy_use_coupon = user3_feature.buy_use_coupon.replace(np.nan,0)\n",
    "user3_feature['buy_use_coupon_rate'] = user3_feature.buy_use_coupon.astype('float') / user3_feature.buy_total.astype('float')\n",
    "user3_feature['user_coupon_transfer_rate'] = user3_feature.buy_use_coupon.astype('float') / user3_feature.coupon_received.astype('float')\n",
    "user3_feature.buy_total = user3_feature.buy_total.replace(np.nan,0)\n",
    "user3_feature.coupon_received = user3_feature.coupon_received.replace(np.nan,0)\n",
    "user3_feature.to_csv('data/user3_feature.csv',index=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:49:06.461871Z",
     "start_time": "2023-12-28T00:49:03.136582Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/1641680862.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t.drop_duplicates(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "#for dataset2\n",
    "user2 = feature2[['user_id','merchant_id','coupon_id','discount_rate','distance','date_received','date']]\n",
    "\n",
    "t = user2[['user_id']]\n",
    "t.drop_duplicates(inplace=True)\n",
    "\n",
    "t1 = user2[user2.date!='null'][['user_id','merchant_id']]\n",
    "t1.drop_duplicates(inplace=True)\n",
    "t1.merchant_id = 1\n",
    "t1 = t1.groupby('user_id').agg('sum').reset_index()\n",
    "t1.rename(columns={'merchant_id':'count_merchant'},inplace=True)\n",
    "user2['coupon_id']=user3['coupon_id'].replace(np.nan,'null')\n",
    "t2 = user2[(user2.date!='null')&(user2.coupon_id!='null')][['user_id','distance']]\n",
    "t2.replace('null',-1,inplace=True)\n",
    "#t2.distance = t2.distance.astype('int')\n",
    "t2.replace(-1,np.nan,inplace=True)\n",
    "t3 = t2.groupby('user_id').agg('min').reset_index()\n",
    "t3.rename(columns={'distance':'user_min_distance'},inplace=True)\n",
    "\n",
    "t4 = t2.groupby('user_id').agg('max').reset_index()\n",
    "t4.rename(columns={'distance':'user_max_distance'},inplace=True)\n",
    "\n",
    "t5 = t2.groupby('user_id').agg('mean').reset_index()\n",
    "t5.rename(columns={'distance':'user_mean_distance'},inplace=True)\n",
    "\n",
    "t6 = t2.groupby('user_id').agg('median').reset_index()\n",
    "t6.rename(columns={'distance':'user_median_distance'},inplace=True)\n",
    "\n",
    "t7 = user2[(user2.date!='null')&(user2.coupon_id!='null')][['user_id']]\n",
    "t7['buy_use_coupon'] = 1\n",
    "t7 = t7.groupby('user_id').agg('sum').reset_index()\n",
    "\n",
    "t8 = user2[user2.date!='null'][['user_id']]\n",
    "t8['buy_total'] = 1\n",
    "t8 = t8.groupby('user_id').agg('sum').reset_index()\n",
    "\n",
    "t9 = user2[user2.coupon_id!='null'][['user_id']]\n",
    "t9['coupon_received'] = 1\n",
    "t9 = t9.groupby('user_id').agg('sum').reset_index()\n",
    "\n",
    "t10 = user2[(user2.date_received!='null')&(user2.date!='null')][['user_id','date_received','date']]\n",
    "t10['user_date_datereceived_gap'] = t10.date + ':' + t10.date_received\n",
    "t10.user_date_datereceived_gap = t10.user_date_datereceived_gap.apply(get_user_date_datereceived_gap)\n",
    "t10 = t10[['user_id','user_date_datereceived_gap']]\n",
    "\n",
    "t11 = t10.groupby('user_id').agg('mean').reset_index()\n",
    "t11.rename(columns={'user_date_datereceived_gap':'avg_user_date_datereceived_gap'},inplace=True)\n",
    "t12 = t10.groupby('user_id').agg('min').reset_index()\n",
    "t12.rename(columns={'user_date_datereceived_gap':'min_user_date_datereceived_gap'},inplace=True)\n",
    "t13 = t10.groupby('user_id').agg('max').reset_index()\n",
    "t13.rename(columns={'user_date_datereceived_gap':'max_user_date_datereceived_gap'},inplace=True)\n",
    "\n",
    "user2_feature = pd.merge(t,t1,on='user_id',how='left')\n",
    "user2_feature = pd.merge(user2_feature,t3,on='user_id',how='left')\n",
    "user2_feature = pd.merge(user2_feature,t4,on='user_id',how='left')\n",
    "user2_feature = pd.merge(user2_feature,t5,on='user_id',how='left')\n",
    "user2_feature = pd.merge(user2_feature,t6,on='user_id',how='left')\n",
    "user2_feature = pd.merge(user2_feature,t7,on='user_id',how='left')\n",
    "user2_feature = pd.merge(user2_feature,t8,on='user_id',how='left')\n",
    "user2_feature = pd.merge(user2_feature,t9,on='user_id',how='left')\n",
    "user2_feature = pd.merge(user2_feature,t11,on='user_id',how='left')\n",
    "user2_feature = pd.merge(user2_feature,t12,on='user_id',how='left')\n",
    "user2_feature = pd.merge(user2_feature,t13,on='user_id',how='left')\n",
    "user2_feature.count_merchant = user2_feature.count_merchant.replace(np.nan,0)\n",
    "user2_feature.buy_use_coupon = user2_feature.buy_use_coupon.replace(np.nan,0)\n",
    "user2_feature['buy_use_coupon_rate'] = user2_feature.buy_use_coupon.astype('float') / user2_feature.buy_total.astype('float')\n",
    "user2_feature['user_coupon_transfer_rate'] = user2_feature.buy_use_coupon.astype('float') / user2_feature.coupon_received.astype('float')\n",
    "user2_feature.buy_total = user2_feature.buy_total.replace(np.nan,0)\n",
    "user2_feature.coupon_received = user2_feature.coupon_received.replace(np.nan,0)\n",
    "user2_feature.to_csv('data/user2_feature.csv',index=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:49:24.494235Z",
     "start_time": "2023-12-28T00:49:20.668530Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2639381647.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t.drop_duplicates(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "#for dataset1\n",
    "user1 = feature1[['user_id','merchant_id','coupon_id','discount_rate','distance','date_received','date']]\n",
    "\n",
    "t = user1[['user_id']]\n",
    "t.drop_duplicates(inplace=True)\n",
    "\n",
    "t1 = user1[user1.date!='null'][['user_id','merchant_id']]\n",
    "t1.drop_duplicates(inplace=True)\n",
    "t1.merchant_id = 1\n",
    "t1 = t1.groupby('user_id').agg('sum').reset_index()\n",
    "t1.rename(columns={'merchant_id':'count_merchant'},inplace=True)\n",
    "user1['coupon_id']=user3['coupon_id'].replace(np.nan,'null')\n",
    "t2 = user1[(user1.date!='null')&(user1.coupon_id!='null')][['user_id','distance']]\n",
    "t2.replace('null',-1,inplace=True)\n",
    "#t2.distance = t2.distance.astype('int')\n",
    "t2.replace(-1,np.nan,inplace=True)\n",
    "t3 = t2.groupby('user_id').agg('min').reset_index()\n",
    "t3.rename(columns={'distance':'user_min_distance'},inplace=True)\n",
    "\n",
    "t4 = t2.groupby('user_id').agg('max').reset_index()\n",
    "t4.rename(columns={'distance':'user_max_distance'},inplace=True)\n",
    "\n",
    "t5 = t2.groupby('user_id').agg('mean').reset_index()\n",
    "t5.rename(columns={'distance':'user_mean_distance'},inplace=True)\n",
    "\n",
    "t6 = t2.groupby('user_id').agg('median').reset_index()\n",
    "t6.rename(columns={'distance':'user_median_distance'},inplace=True)\n",
    "\n",
    "t7 = user1[(user1.date!='null')&(user1.coupon_id!='null')][['user_id']]\n",
    "t7['buy_use_coupon'] = 1\n",
    "t7 = t7.groupby('user_id').agg('sum').reset_index()\n",
    "\n",
    "t8 = user1[user1.date!='null'][['user_id']]\n",
    "t8['buy_total'] = 1\n",
    "t8 = t8.groupby('user_id').agg('sum').reset_index()\n",
    "\n",
    "t9 = user1[user1.coupon_id!='null'][['user_id']]\n",
    "t9['coupon_received'] = 1\n",
    "t9 = t9.groupby('user_id').agg('sum').reset_index()\n",
    "\n",
    "t10 = user1[(user1.date_received!='null')&(user1.date!='null')][['user_id','date_received','date']]\n",
    "t10['user_date_datereceived_gap'] = t10.date + ':' + t10.date_received\n",
    "t10.user_date_datereceived_gap = t10.user_date_datereceived_gap.apply(get_user_date_datereceived_gap)\n",
    "t10 = t10[['user_id','user_date_datereceived_gap']]\n",
    "\n",
    "t11 = t10.groupby('user_id').agg('mean').reset_index()\n",
    "t11.rename(columns={'user_date_datereceived_gap':'avg_user_date_datereceived_gap'},inplace=True)\n",
    "t12 = t10.groupby('user_id').agg('min').reset_index()\n",
    "t12.rename(columns={'user_date_datereceived_gap':'min_user_date_datereceived_gap'},inplace=True)\n",
    "t13 = t10.groupby('user_id').agg('max').reset_index()\n",
    "t13.rename(columns={'user_date_datereceived_gap':'max_user_date_datereceived_gap'},inplace=True)\n",
    "\n",
    "user1_feature = pd.merge(t,t1,on='user_id',how='left')\n",
    "user1_feature = pd.merge(user1_feature,t3,on='user_id',how='left')\n",
    "user1_feature = pd.merge(user1_feature,t4,on='user_id',how='left')\n",
    "user1_feature = pd.merge(user1_feature,t5,on='user_id',how='left')\n",
    "user1_feature = pd.merge(user1_feature,t6,on='user_id',how='left')\n",
    "user1_feature = pd.merge(user1_feature,t7,on='user_id',how='left')\n",
    "user1_feature = pd.merge(user1_feature,t8,on='user_id',how='left')\n",
    "user1_feature = pd.merge(user1_feature,t9,on='user_id',how='left')\n",
    "user1_feature = pd.merge(user1_feature,t11,on='user_id',how='left')\n",
    "user1_feature = pd.merge(user1_feature,t12,on='user_id',how='left')\n",
    "user1_feature = pd.merge(user1_feature,t13,on='user_id',how='left')\n",
    "user1_feature.count_merchant = user1_feature.count_merchant.replace(np.nan,0)\n",
    "user1_feature.buy_use_coupon = user1_feature.buy_use_coupon.replace(np.nan,0)\n",
    "user1_feature['buy_use_coupon_rate'] = user1_feature.buy_use_coupon.astype('float') / user1_feature.buy_total.astype('float')\n",
    "user1_feature['user_coupon_transfer_rate'] = user1_feature.buy_use_coupon.astype('float') / user1_feature.coupon_received.astype('float')\n",
    "user1_feature.buy_total = user1_feature.buy_total.replace(np.nan,0)\n",
    "user1_feature.coupon_received = user1_feature.coupon_received.replace(np.nan,0)\n",
    "user1_feature.to_csv('data/user1_feature.csv',index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User_merchant related features\n",
    "times_user_buy_merchant_before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:51:08.604562Z",
     "start_time": "2023-12-28T00:51:04.839743Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2574517091.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  all_user_merchant.drop_duplicates(inplace=True)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2574517091.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t1.coupon_id=t1.coupon_id.replace(np.nan,'null')\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2574517091.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t3['user_merchant_any'] = 1\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/2574517091.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t4.coupon_id=t4.coupon_id.replace(np.nan,'null')\n"
     ]
    }
   ],
   "source": [
    "#for dataset3\n",
    "all_user_merchant = feature3[['user_id','merchant_id']]\n",
    "all_user_merchant.drop_duplicates(inplace=True)\n",
    "\n",
    "t = feature3[['user_id','merchant_id','date']]\n",
    "t = t[t.date!='null'][['user_id','merchant_id']]\n",
    "t['user_merchant_buy_total'] = 1\n",
    "t = t.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t.drop_duplicates(inplace=True)\n",
    "\n",
    "t1 = feature3[['user_id','merchant_id','coupon_id']]\n",
    "t1.coupon_id=t1.coupon_id.replace(np.nan,'null')\n",
    "t1 = t1[t1.coupon_id!='null'][['user_id','merchant_id']]\n",
    "t1['user_merchant_received'] = 1\n",
    "t1 = t1.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t1.drop_duplicates(inplace=True)\n",
    "\n",
    "t2 = feature3[['user_id','merchant_id','date','date_received']]\n",
    "t2 = t2[(t2.date!='null')&(t2.date_received!='null')][['user_id','merchant_id']]\n",
    "t2['user_merchant_buy_use_coupon'] = 1\n",
    "t2 = t2.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t2.drop_duplicates(inplace=True)\n",
    "\n",
    "t3 = feature3[['user_id','merchant_id']]\n",
    "t3['user_merchant_any'] = 1\n",
    "t3 = t3.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t3.drop_duplicates(inplace=True)\n",
    "\n",
    "t4 = feature3[['user_id','merchant_id','date','coupon_id']]\n",
    "t4.coupon_id=t4.coupon_id.replace(np.nan,'null')\n",
    "t4 = t4[(t4.date!='null')&(t4.coupon_id=='null')][['user_id','merchant_id']]\n",
    "t4['user_merchant_buy_common'] = 1\n",
    "t4 = t4.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t4.drop_duplicates(inplace=True)\n",
    "\n",
    "user_merchant3 = pd.merge(all_user_merchant,t,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant3 = pd.merge(user_merchant3,t1,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant3 = pd.merge(user_merchant3,t2,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant3 = pd.merge(user_merchant3,t3,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant3 = pd.merge(user_merchant3,t4,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant3.user_merchant_buy_use_coupon = user_merchant3.user_merchant_buy_use_coupon.replace(np.nan,0)\n",
    "user_merchant3.user_merchant_buy_common = user_merchant3.user_merchant_buy_common.replace(np.nan,0)\n",
    "user_merchant3['user_merchant_coupon_transfer_rate'] = user_merchant3.user_merchant_buy_use_coupon.astype('float') / user_merchant3.user_merchant_received.astype('float')\n",
    "user_merchant3['user_merchant_coupon_buy_rate'] = user_merchant3.user_merchant_buy_use_coupon.astype('float') / user_merchant3.user_merchant_buy_total.astype('float')\n",
    "user_merchant3['user_merchant_rate'] = user_merchant3.user_merchant_buy_total.astype('float') / user_merchant3.user_merchant_any.astype('float')\n",
    "user_merchant3['user_merchant_common_buy_rate'] = user_merchant3.user_merchant_buy_common.astype('float') / user_merchant3.user_merchant_buy_total.astype('float')\n",
    "user_merchant3.to_csv('data/user_merchant3.csv',index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:51:20.618450Z",
     "start_time": "2023-12-28T00:51:17.562146Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3256679969.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  all_user_merchant.drop_duplicates(inplace=True)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3256679969.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t1.coupon_id=t1.coupon_id.replace(np.nan,'null')\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3256679969.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t3['user_merchant_any'] = 1\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3256679969.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t4.coupon_id=t4.coupon_id.replace(np.nan,'null')\n"
     ]
    }
   ],
   "source": [
    "#for dataset2\n",
    "all_user_merchant = feature2[['user_id','merchant_id']]\n",
    "all_user_merchant.drop_duplicates(inplace=True)\n",
    "\n",
    "t = feature2[['user_id','merchant_id','date']]\n",
    "t = t[t.date!='null'][['user_id','merchant_id']]\n",
    "t['user_merchant_buy_total'] = 1\n",
    "t = t.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t.drop_duplicates(inplace=True)\n",
    "\n",
    "t1 = feature2[['user_id','merchant_id','coupon_id']]\n",
    "t1.coupon_id=t1.coupon_id.replace(np.nan,'null')\n",
    "t1 = t1[t1.coupon_id!='null'][['user_id','merchant_id']]\n",
    "t1['user_merchant_received'] = 1\n",
    "t1 = t1.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t1.drop_duplicates(inplace=True)\n",
    "\n",
    "t2 = feature2[['user_id','merchant_id','date','date_received']]\n",
    "t2 = t2[(t2.date!='null')&(t2.date_received!='null')][['user_id','merchant_id']]\n",
    "t2['user_merchant_buy_use_coupon'] = 1\n",
    "t2 = t2.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t2.drop_duplicates(inplace=True)\n",
    "\n",
    "t3 = feature2[['user_id','merchant_id']]\n",
    "t3['user_merchant_any'] = 1\n",
    "t3 = t3.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t3.drop_duplicates(inplace=True)\n",
    "\n",
    "t4 = feature2[['user_id','merchant_id','date','coupon_id']]\n",
    "t4.coupon_id=t4.coupon_id.replace(np.nan,'null')\n",
    "t4 = t4[(t4.date!='null')&(t4.coupon_id=='null')][['user_id','merchant_id']]\n",
    "t4['user_merchant_buy_common'] = 1\n",
    "t4 = t4.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t4.drop_duplicates(inplace=True)\n",
    "\n",
    "user_merchant2 = pd.merge(all_user_merchant,t,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant2 = pd.merge(user_merchant2,t1,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant2 = pd.merge(user_merchant2,t2,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant2 = pd.merge(user_merchant2,t3,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant2 = pd.merge(user_merchant2,t4,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant2.user_merchant_buy_use_coupon = user_merchant2.user_merchant_buy_use_coupon.replace(np.nan,0)\n",
    "user_merchant2.user_merchant_buy_common = user_merchant2.user_merchant_buy_common.replace(np.nan,0)\n",
    "user_merchant2['user_merchant_coupon_transfer_rate'] = user_merchant2.user_merchant_buy_use_coupon.astype('float') / user_merchant2.user_merchant_received.astype('float')\n",
    "user_merchant2['user_merchant_coupon_buy_rate'] = user_merchant2.user_merchant_buy_use_coupon.astype('float') / user_merchant2.user_merchant_buy_total.astype('float')\n",
    "user_merchant2['user_merchant_rate'] = user_merchant2.user_merchant_buy_total.astype('float') / user_merchant2.user_merchant_any.astype('float')\n",
    "user_merchant2['user_merchant_common_buy_rate'] = user_merchant2.user_merchant_buy_common.astype('float') / user_merchant2.user_merchant_buy_total.astype('float')\n",
    "user_merchant2.to_csv('data/user_merchant2.csv',index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:51:57.811693Z",
     "start_time": "2023-12-28T00:51:53.545348Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3294301674.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  all_user_merchant.drop_duplicates(inplace=True)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3294301674.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t1.coupon_id=t1.coupon_id.replace(np.nan,'null')\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3294301674.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t3['user_merchant_any'] = 1\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/3294301674.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  t4.coupon_id=t4.coupon_id.replace(np.nan,'null')\n"
     ]
    }
   ],
   "source": [
    "#for dataset1\n",
    "all_user_merchant = feature1[['user_id','merchant_id']]\n",
    "all_user_merchant.drop_duplicates(inplace=True)\n",
    "\n",
    "t = feature1[['user_id','merchant_id','date']]\n",
    "t = t[t.date!='null'][['user_id','merchant_id']]\n",
    "t['user_merchant_buy_total'] = 1\n",
    "t = t.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t.drop_duplicates(inplace=True)\n",
    "\n",
    "t1 = feature1[['user_id','merchant_id','coupon_id']]\n",
    "t1.coupon_id=t1.coupon_id.replace(np.nan,'null')\n",
    "t1 = t1[t1.coupon_id!='null'][['user_id','merchant_id']]\n",
    "t1['user_merchant_received'] = 1\n",
    "t1 = t1.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t1.drop_duplicates(inplace=True)\n",
    "\n",
    "t2 = feature1[['user_id','merchant_id','date','date_received']]\n",
    "t2 = t2[(t2.date!='null')&(t2.date_received!='null')][['user_id','merchant_id']]\n",
    "t2['user_merchant_buy_use_coupon'] = 1\n",
    "t2 = t2.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t2.drop_duplicates(inplace=True)\n",
    "\n",
    "t3 = feature1[['user_id','merchant_id']]\n",
    "t3['user_merchant_any'] = 1\n",
    "t3 = t3.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t3.drop_duplicates(inplace=True)\n",
    "\n",
    "t4 = feature1[['user_id','merchant_id','date','coupon_id']]\n",
    "t4.coupon_id=t4.coupon_id.replace(np.nan,'null')\n",
    "t4 = t4[(t4.date!='null')&(t4.coupon_id=='null')][['user_id','merchant_id']]\n",
    "t4['user_merchant_buy_common'] = 1\n",
    "t4 = t4.groupby(['user_id','merchant_id']).agg('sum').reset_index()\n",
    "t4.drop_duplicates(inplace=True)\n",
    "\n",
    "user_merchant1 = pd.merge(all_user_merchant,t,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant1 = pd.merge(user_merchant1,t1,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant1 = pd.merge(user_merchant1,t2,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant1 = pd.merge(user_merchant1,t3,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant1 = pd.merge(user_merchant1,t4,on=['user_id','merchant_id'],how='left')\n",
    "user_merchant1.user_merchant_buy_use_coupon = user_merchant1.user_merchant_buy_use_coupon.replace(np.nan,0)\n",
    "user_merchant1.user_merchant_buy_common = user_merchant1.user_merchant_buy_common.replace(np.nan,0)\n",
    "user_merchant1['user_merchant_coupon_transfer_rate'] = user_merchant1.user_merchant_buy_use_coupon.astype('float') / user_merchant1.user_merchant_received.astype('float')\n",
    "user_merchant1['user_merchant_coupon_buy_rate'] = user_merchant1.user_merchant_buy_use_coupon.astype('float') / user_merchant1.user_merchant_buy_total.astype('float')\n",
    "user_merchant1['user_merchant_rate'] = user_merchant1.user_merchant_buy_total.astype('float') / user_merchant1.user_merchant_any.astype('float')\n",
    "user_merchant1['user_merchant_common_buy_rate'] = user_merchant1.user_merchant_buy_common.astype('float') / user_merchant1.user_merchant_buy_total.astype('float')\n",
    "user_merchant1.to_csv('data/user_merchant1.csv',index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:52:29.448729Z",
     "start_time": "2023-12-28T00:52:29.435214Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def get_label(s):\n",
    "    s = s.split(':')\n",
    "    if s[0]=='nan':\n",
    "        return 0\n",
    "    elif (date(int(s[0][0:4]),int(s[0][4:6]),int(s[0][6:8]))-date(int(s[1][0:4]),int(s[1][4:6]),int(s[1][6:8]))).days<=15:\n",
    "        return 1\n",
    "    else:\n",
    "        return -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:52:45.318081Z",
     "start_time": "2023-12-28T00:52:42.004841Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(112803, 52)\n"
     ]
    }
   ],
   "source": [
    "coupon3 = pd.read_csv('data/coupon3_feature.csv')\n",
    "merchant3 = pd.read_csv('data/merchant3_feature.csv')\n",
    "user3 = pd.read_csv('data/user3_feature.csv')\n",
    "user_merchant3 = pd.read_csv('data/user_merchant3.csv')\n",
    "other_feature3 = pd.read_csv('data/other_feature3.csv')\n",
    "dataset3 = pd.merge(coupon3,merchant3,on='merchant_id',how='left')\n",
    "dataset3 = pd.merge(dataset3,user3,on='user_id',how='left')\n",
    "dataset3 = pd.merge(dataset3,user_merchant3,on=['user_id','merchant_id'],how='left')\n",
    "dataset3 = pd.merge(dataset3,other_feature3,on=['user_id','coupon_id','date_received'],how='left')\n",
    "dataset3.drop_duplicates(inplace=True)\n",
    "print (dataset3.shape)\n",
    "\n",
    "dataset3.user_merchant_buy_total = dataset3.user_merchant_buy_total.replace(np.nan,0)\n",
    "dataset3.user_merchant_any = dataset3.user_merchant_any.replace(np.nan,0)\n",
    "dataset3.user_merchant_received = dataset3.user_merchant_received.replace(np.nan,0)\n",
    "dataset3['is_weekend'] = dataset3.day_of_week.apply(lambda x:1 if x in (6,7) else 0)\n",
    "weekday_dummies = pd.get_dummies(dataset3.day_of_week)\n",
    "weekday_dummies.columns = ['weekday'+str(i+1) for i in range(weekday_dummies.shape[1])]\n",
    "dataset3 = pd.concat([dataset3,weekday_dummies],axis=1)\n",
    "dataset3.drop(['merchant_id','day_of_week','coupon_count'],axis=1,inplace=True)\n",
    "dataset3 = dataset3.replace('null',np.nan)\n",
    "dataset3.to_csv('data/dataset3.csv',index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:52:58.272448Z",
     "start_time": "2023-12-28T00:52:51.412249Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(253670, 53)\n"
     ]
    }
   ],
   "source": [
    "coupon2 = pd.read_csv('data/coupon2_feature.csv')\n",
    "merchant2 = pd.read_csv('data/merchant2_feature.csv')\n",
    "user2 = pd.read_csv('data/user2_feature.csv')\n",
    "user_merchant2 = pd.read_csv('data/user_merchant2.csv')\n",
    "other_feature2 = pd.read_csv('data/other_feature2.csv')\n",
    "dataset2 = pd.merge(coupon2,merchant2,on='merchant_id',how='left')\n",
    "dataset2 = pd.merge(dataset2,user2,on='user_id',how='left')\n",
    "dataset2 = pd.merge(dataset2,user_merchant2,on=['user_id','merchant_id'],how='left')\n",
    "dataset2 = pd.merge(dataset2,other_feature2,on=['user_id','coupon_id','date_received'],how='left')\n",
    "dataset2.drop_duplicates(inplace=True)\n",
    "print( dataset2.shape)\n",
    "\n",
    "dataset2.user_merchant_buy_total = dataset2.user_merchant_buy_total.replace(np.nan,0)\n",
    "dataset2.user_merchant_any = dataset2.user_merchant_any.replace(np.nan,0)\n",
    "dataset2.user_merchant_received = dataset2.user_merchant_received.replace(np.nan,0)\n",
    "dataset2['is_weekend'] = dataset2.day_of_week.apply(lambda x:1 if x in (6,7) else 0)\n",
    "weekday_dummies = pd.get_dummies(dataset2.day_of_week)\n",
    "weekday_dummies.columns = ['weekday'+str(i+1) for i in range(weekday_dummies.shape[1])]\n",
    "dataset2 = pd.concat([dataset2,weekday_dummies],axis=1)\n",
    "dataset2['label'] = dataset2.date.astype('str') + ':' +  dataset2.date_received.astype('str')\n",
    "dataset2.label = dataset2.label.apply(get_label)\n",
    "dataset2.drop(['merchant_id','day_of_week','date','date_received','coupon_id','coupon_count'],axis=1,inplace=True)\n",
    "dataset2 = dataset2.replace('null',np.nan)\n",
    "dataset2.to_csv('data/dataset2.csv',index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:53:04.551164Z",
     "start_time": "2023-12-28T00:53:00.759225Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(130957, 53)\n"
     ]
    }
   ],
   "source": [
    "coupon1 = pd.read_csv('data/coupon1_feature.csv')\n",
    "merchant1 = pd.read_csv('data/merchant1_feature.csv')\n",
    "user1 = pd.read_csv('data/user1_feature.csv')\n",
    "user_merchant1 = pd.read_csv('data/user_merchant1.csv')\n",
    "other_feature1 = pd.read_csv('data/other_feature1.csv')\n",
    "dataset1 = pd.merge(coupon1,merchant1,on='merchant_id',how='left')\n",
    "dataset1 = pd.merge(dataset1,user1,on='user_id',how='left')\n",
    "dataset1 = pd.merge(dataset1,user_merchant1,on=['user_id','merchant_id'],how='left')\n",
    "dataset1 = pd.merge(dataset1,other_feature1,on=['user_id','coupon_id','date_received'],how='left')\n",
    "dataset1.drop_duplicates(inplace=True)\n",
    "print (dataset1.shape)\n",
    "\n",
    "dataset1.user_merchant_buy_total = dataset1.user_merchant_buy_total.replace(np.nan,0)\n",
    "dataset1.user_merchant_any = dataset1.user_merchant_any.replace(np.nan,0)\n",
    "dataset1.user_merchant_received = dataset1.user_merchant_received.replace(np.nan,0)\n",
    "dataset1['is_weekend'] = dataset1.day_of_week.apply(lambda x:1 if x in (6,7) else 0)\n",
    "weekday_dummies = pd.get_dummies(dataset1.day_of_week)\n",
    "weekday_dummies.columns = ['weekday'+str(i+1) for i in range(weekday_dummies.shape[1])]\n",
    "dataset1 = pd.concat([dataset1,weekday_dummies],axis=1)\n",
    "dataset1['label'] = dataset1.date.astype('str') + ':' +  dataset1.date_received.astype('str')\n",
    "dataset1.label = dataset1.label.apply(get_label)\n",
    "dataset1.drop(['merchant_id','day_of_week','date','date_received','coupon_id','coupon_count'],axis=1,inplace=True)\n",
    "dataset1 = dataset1.replace('null',np.nan)\n",
    "dataset1.to_csv('data/dataset1.csv',index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning\n",
    "\n",
    "(Gradient-boosted decision trees, GBDT)\n",
    "\n",
    "关于调参：[https://blog.csdn.net/sinat_35512245/article/details/79700029](https://blog.csdn.net/sinat_35512245/article/details/79700029)\n",
    "\n",
    "GBDT调参顺序 可使用GridSearchCV\n",
    "1. n_estimators & learning_rate\n",
    "2. max_depth 或可为1\n",
    "3. min_samples_split & min_samples_leaf\n",
    "4. max_features 特征较多时考虑取部分\n",
    "5. subsample 使用样本的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:53:51.598408Z",
     "start_time": "2023-12-28T00:53:50.855633Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import ensemble\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# from sklearn.grid_search import GridSearchCV\n",
    "import pickle\n",
    "import os\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:54:01.972895Z",
     "start_time": "2023-12-28T00:54:00.779042Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset1 = pd.read_csv('data/dataset1.csv')\n",
    "dataset1.label.replace(-1,0,inplace=True)\n",
    "dataset2 = pd.read_csv('data/dataset2.csv')\n",
    "dataset2.label.replace(-1,0,inplace=True)\n",
    "dataset3 = pd.read_csv('data/dataset3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:54:10.761752Z",
     "start_time": "2023-12-28T00:54:09.716262Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset1.drop_duplicates(inplace=True)\n",
    "dataset2.drop_duplicates(inplace=True)\n",
    "dataset3.drop_duplicates(inplace=True)\n",
    "\n",
    "dataset12 = pd.concat([dataset1,dataset2],axis=0)\n",
    "\n",
    "dataset1_y = dataset1.label\n",
    "dataset1_x = dataset1.drop(['user_id','label','day_gap_before','day_gap_after'],axis=1)  # 'day_gap_before','day_gap_after' cause overfitting, 0.77\n",
    "dataset2_y = dataset2.label\n",
    "dataset2_x = dataset2.drop(['user_id','label','day_gap_before','day_gap_after'],axis=1)\n",
    "dataset12_y = dataset12.label\n",
    "dataset12_x = dataset12.drop(['user_id','label','day_gap_before','day_gap_after'],axis=1)\n",
    "dataset3_preds = dataset3[['user_id','coupon_id','date_received']]\n",
    "dataset3_x = dataset3.drop(['user_id','coupon_id','date_received','day_gap_before','day_gap_after'],axis=1)\n",
    "\n",
    "dataset12_x=dataset12_x.fillna(-100)\n",
    "dataset3_x=dataset3_x.fillna(-100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 27 candidates, totalling 135 fits\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_79094/4139634410.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m }\n\u001b[1;32m      7\u001b[0m \u001b[0mgrid_search\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensemble\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGradientBoostingRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'neg_mean_squared_error'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset12_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset12_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    889\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 891\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1390\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1391\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1392\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    836\u001b[0m                     )\n\u001b[1;32m    837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 838\u001b[0;31m                 out = parallel(\n\u001b[0m\u001b[1;32m    839\u001b[0m                     delayed(_fit_and_score)(\n\u001b[1;32m    840\u001b[0m                         \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_estimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1044\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1046\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1047\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    859\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    860\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 861\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    862\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    777\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    780\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    678\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_gb.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, monitor)\u001b[0m\n\u001b[1;32m    584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m         \u001b[0;31m# fit the boosting stages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m         n_stages = self._fit_stages(\n\u001b[0m\u001b[1;32m    587\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_gb.py\u001b[0m in \u001b[0;36m_fit_stages\u001b[0;34m(self, X, y, raw_predictions, sample_weight, random_state, X_val, y_val, sample_weight_val, begin_at_stage, monitor)\u001b[0m\n\u001b[1;32m    661\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m             \u001b[0;31m# fit next stage of trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 663\u001b[0;31m             raw_predictions = self._fit_stage(\n\u001b[0m\u001b[1;32m    664\u001b[0m                 \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_gb.py\u001b[0m in \u001b[0;36m_fit_stage\u001b[0;34m(self, i, X, y, raw_predictions, sample_weight, sample_mask, random_state, X_csc, X_csr)\u001b[0m\n\u001b[1;32m    244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_csr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX_csr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 246\u001b[0;31m             \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresidual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m             \u001b[0;31m# update tree leaves\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1313\u001b[0m         \"\"\"\n\u001b[1;32m   1314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1315\u001b[0;31m         super().fit(\n\u001b[0m\u001b[1;32m   1316\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1317\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    418\u001b[0m             )\n\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# gridsearch\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 150, 200],\n",
    "    'max_depth': [3, 4, 5],\n",
    "    'learning_rate': [0.1, 0.11, 0.12]\n",
    "}\n",
    "grid_search = GridSearchCV(ensemble.GradientBoostingRegressor(), param_grid, cv=5, scoring='neg_mean_squared_error', verbose=1)\n",
    "grid_search.fit(dataset12_x, dataset12_y)\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_estimator_)\n",
    "print(grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用gridsearch的参数来算结果\n",
    "dataset3_preds['label'] = grid_search.predict(dataset3_x)\n",
    "dataset3_preds.label = MinMaxScaler().fit_transform(dataset3_preds.label.values.reshape(-1, 1))\n",
    "dataset3_preds.sort_values(by=['coupon_id','label'],inplace=True)\n",
    "dataset3_preds.to_csv(\"results\"+datetime.datetime.now().strftime('%Y-%m-%d_%H_%M_%S')+\".csv\",index=None,header=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:56:36.834157Z",
     "start_time": "2023-12-28T00:54:23.065399Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jizhengyu/opt/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:286: FutureWarning: The loss 'ls' was deprecated in v1.0 and will be removed in version 1.2. Use 'squared_error' which is equivalent.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Iter       Train Loss   Remaining Time \n",
      "         1           0.0716            2.21m\n",
      "         2           0.0690            2.18m\n",
      "         3           0.0671            2.15m\n",
      "         4           0.0654            2.14m\n",
      "         5           0.0640            2.12m\n",
      "         6           0.0629            2.12m\n",
      "         7           0.0619            2.12m\n",
      "         8           0.0612            2.10m\n",
      "         9           0.0605            2.09m\n",
      "        10           0.0599            2.07m\n",
      "        20           0.0570            1.91m\n",
      "        30           0.0558            1.78m\n",
      "        40           0.0551            1.66m\n",
      "        50           0.0546            1.52m\n",
      "        60           0.0543            1.38m\n",
      "        70           0.0540            1.24m\n",
      "        80           0.0538            1.11m\n",
      "        90           0.0536           58.19s\n",
      "       100           0.0534           49.87s\n"
     ]
    }
   ],
   "source": [
    "score=[]\n",
    "# 深度调参\n",
    "#gbdt=ensemble.GradientBoostingRegressor(\n",
    "#  loss='ls'\n",
    "#, learning_rate=0.1\n",
    "#, n_estimators=1000\n",
    "#, subsample=1\n",
    "#, min_samples_split=15\n",
    "#, min_samples_leaf=10\n",
    "#, max_depth=3\n",
    "#, init=None\n",
    "#, random_state=None\n",
    "#, max_features=None\n",
    "#, alpha=0.9\n",
    "#, verbose=1\n",
    "#, max_leaf_nodes=None\n",
    "#, warm_start=False)\n",
    "#gbdt.fit(dataset12_x,dataset12_y)\n",
    "#\n",
    "#score.append(gbdt.score(dataset12_x,dataset12_y))\n",
    "\n",
    "#dataset3_preds['label'] = gbdt.predict(dataset3_x)\n",
    "#dataset3_preds.label = MinMaxScaler().fit_transform(dataset3_preds.label.reshape(-1, 1))\n",
    "#dataset3_preds.sort_values(by=['coupon_id','label'],inplace=True)\n",
    "#dataset3_preds.to_csv(\"test.csv\",index=None,header=None)\n",
    "\n",
    "# 深度调参\n",
    "gbdt=ensemble.GradientBoostingRegressor(\n",
    "  loss='ls'\n",
    ", learning_rate=0.11\n",
    ", n_estimators=160\n",
    ", subsample=1\n",
    ", min_samples_split=4  #4 is best\n",
    ", min_samples_leaf=1\n",
    ", max_depth=4\n",
    ", init=None\n",
    ", random_state=None\n",
    ", max_features=None\n",
    ", alpha=0.9b\n",
    ", verbose=1\n",
    ", max_leaf_nodes=None\n",
    ", warm_start=False)\n",
    "gbdt.fit(dataset12_x,dataset12_y)\n",
    "\n",
    "score.append(gbdt.score(dataset12_x,dataset12_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-12-28T00:57:08.266118Z",
     "start_time": "2023-12-28T00:57:07.748029Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/4029169744.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset3_preds['label'] = gbdt.predict(dataset3_x)\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/4029169744.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset3_preds.label = MinMaxScaler().fit_transform(dataset3_preds.label.values.reshape(-1, 1))\n",
      "/var/folders/j1/ghhyxs356b1gh5xw45_pfnxr0000gn/T/ipykernel_30010/4029169744.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset3_preds.sort_values(by=['coupon_id','label'],inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# 用上面手动的参数训练出来的模型算结果\n",
    "dataset3_preds['label'] = gbdt.predict(dataset3_x)\n",
    "dataset3_preds.label = MinMaxScaler().fit_transform(dataset3_preds.label.values.reshape(-1, 1))\n",
    "dataset3_preds.sort_values(by=['coupon_id','label'],inplace=True)\n",
    "dataset3_preds.to_csv(\"results\"+datetime.datetime.now().strftime('%Y-%m-%d_%H_%M_%S')+\".csv\",index=None,header=None)\n",
    "\n",
    "#os.system('shutdown -s -t 10')"
   ]
  },
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   "source": []
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